Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients.

IF 2.3 3区生物学 Q2 MULTIDISCIPLINARY SCIENCES

PeerJ Pub Date : 2025-04-23 eCollection Date: 2025-01-01 DOI:10.7717/peerj.19351

Wenqing Zhong, Ziyin Zhao, Xin Fang, Jingyi Sun, Yanbing Wei, Fengda Li, Bing Han, Cheng Jin

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引用次数: 0

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide, and early pathological diagnosis is crucial for formulating treatment plans. Despite the widespread attention to pathology in the treatment of HCC patients, a large amount of information contained in pathological images is often overlooked.

Methods: We retrospectively collected clinical data and pathological slide images from (a) 331 HCC patients at Qingdao University Affiliated Hospital between January 2013 and December 2016 and (b) 180 HCC patients from The Cancer Genome Atlas (TCGA). After data screening, precise quantification of various cell types was achieved using QuPath software. Key factors related to the survival prognosis of pathologically confirmed HCC patients were identified through Cox regression and neural network models, and potential therapeutic targets were screened.

Results: Our study showed that tumour-infiltrating lymphocytes (TILs) had a protective effect. We quantified the TILs index by machine learning and built a neural network model to predict the prognostic risk of patients (ROC = 0.836 for training set ROC validation set). 95% CI [0.7688-0.896], and there was a significant difference in prognosis in the high-low risk group predicted by the model (p = 2.6e-18, HR = 0.18, 95% CI [0.12-0.27], and TNFSF4 was identified as a possible immunotherapy target.

Conclusion: This study included a total of 511 patients, divided into a training cohort of 331 cases (from Qingdao University Hospital between January 2013 and December 2016) and a validation cohort of 180 cases (TCGA). The results revealed that tumor-infiltrating lymphocytes (TILs) have a protective effect and successfully predicted the survival risk of liver cancer patients using machine learning and neural network technology. The discovery of TNFSF4 provides a new potential target for immunotherapy.

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构建基于肿瘤浸润淋巴细胞（til）的神经网络模型预测肝癌患者的生存。

背景：肝细胞癌（HCC）是世界范围内最常见的原发性肝癌，早期病理诊断对制定治疗方案至关重要。尽管病理在HCC患者的治疗中得到了广泛的关注，但病理图像中包含的大量信息往往被忽视。方法：回顾性收集(a) 2013年1月至2016年12月青岛大学附属医院331例HCC患者的临床资料和病理切片图像，(b)来自癌症基因组图谱（TCGA）的180例HCC患者。数据筛选后，使用QuPath软件实现各种细胞类型的精确定量。通过Cox回归和神经网络模型确定病理确诊HCC患者生存预后相关的关键因素，筛选潜在的治疗靶点。结果：我们的研究表明肿瘤浸润淋巴细胞（til）具有保护作用。通过机器学习量化TILs指数，建立神经网络模型预测患者预后风险（训练集ROC验证集ROC = 0.836）。95% CI[0.7688-0.896]，模型预测的高-低危组预后差异有统计学意义（p = 2.6e-18, HR = 0.18, 95% CI[0.12-0.27]），确定TNFSF4可能是免疫治疗的靶点。结论：本研究共纳入511例患者，分为培训队列331例（2013年1月至2016年12月青岛大学附属医院）和验证队列180例（TCGA）。结果显示肿瘤浸润淋巴细胞（tumor-浸润lymphotting， TILs）具有保护作用，并利用机器学习和神经网络技术成功预测肝癌患者的生存风险。TNFSF4的发现为免疫治疗提供了一个新的潜在靶点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PeerJ MULTIDISCIPLINARY SCIENCES-

CiteScore

4.70

自引率

3.70%

发文量

1665

审稿时长

10 weeks

期刊介绍： PeerJ is an open access peer-reviewed scientific journal covering research in the biological and medical sciences. At PeerJ, authors take out a lifetime publication plan (for as little as $99) which allows them to publish articles in the journal for free, forever. PeerJ has 5 Nobel Prize Winners on the Board; they have won several industry and media awards; and they are widely recognized as being one of the most interesting recent developments in academic publishing.